import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from tqdm import tqdm
import pickle
import matplotlib.pyplot as plt
# 加载数字数据集
digits = datasets.load_digits()

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)

# 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0
best_k = 0
best_knn_model = None

# 初始化一个列表以存储每个k值的准确率
accuracies = []

# 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in tqdm(range(1, 41), desc="Training KNN models"):
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    y_pred = knn.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    accuracies.append(accuracy)
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_knn_model = knn

# 打印每个k值的准确率
print("每个k值的准确率：", accuracies)

# 打印最佳准确率和相应的k值
print("最佳准确率：", best_accuracy)
print("相应的k值：", best_k)

# 将最佳KNN模型保存到二进制文件
with open('best_knn_model.pkl', 'wb') as f:
    pickle.dump(best_knn_model, f)

# 绘制准确率变化图
plt.figure()
plt.plot(range(1, 41), accuracies, marker='o')
plt.axvline(x=best_k, color='r', linestyle='--')
plt.title('Accuracy vs. Number of Neighbors (k)')
plt.xlabel('Number of Neighbors (k)')
plt.ylabel('Accuracy')
plt.annotate(f'Best Accuracy: {best_accuracy:.2f}', xy=(best_k, best_accuracy), xytext=(best_k+2, best_accuracy-0.01), arrowprops=dict(facecolor='black', shrink=0.05))
plt.savefig('accuracy_plot.pdf')
plt.show()